With the Conformed Dimensions of Data Quality, Dan Myers has created a practicable basis for establishing data quality in companies. The concept goes far beyond the DQ dimensions. In addition, Underlying Concepts are described and named and some of the most relevant scenarios that occur in companies are described. The Conformed Dimensions thus represent a practical approach that makes dealing with data quality in companies manageable.
For the data industry, standardized definitions of data quality dimensions are needed, just as other professions, such as accounting have agreed-upon concepts and terms like “chart of accounts”. It is then up to each organization to gain competitive advantage by the way the dimensions are applied (through assessments, root cause analysis, improvements, metrics, etc.) to manage and increase the quality of the information and data on which the organization depends.
While teaching several classes in Brisbane, Australia recently, I discussed when it is best to start thinking about each of the Conformed Dimensions of Data Quality (CDDQ) within the context of a typical waterfall Software Development Lifecycle (SDLC). We will not cover each dimension per phase here in this blog, but I thought I'd just cover Precision as an example and provide my thoughts on the other dimensions relating to each of the phases as a separate document.
On a recent Southwest flight I was reminded how easy it is for important business processes to fail. The flight attendant came on the speakerphone and politely asked if Eve Adamson (pseudonym) was aboard the aircraft, and if so could ring the attendant call button. If you fly much you know this is a very common occurrence. But if you think about it, this breaks all the rules of common sense and the TSA boarding procedures.
Using the Conformed Dimensions of Data Quality when Training Machine Learning Programs
Over the last year or so, I've noticed improvements in Costco's point of sale process. First, they noticed that items under the shopping cart were sometimes overlooked by checkers, so I saw them install bar codes on the front of each shopping cart- just inches off the ground between the front two wheels. Each checker had to scan the bar code to show that they had checked the bottom for items and scanned them. This seemed to be an improvement and I imagine that this bar code could also be used to inventory the carts as well.
This summer I took a working vacation to San Diego, California. Having spent a week there each year, for many years, I was impressed with the significant increase in number of app-based transportation rentals available all over the streets.
They often refer to Time as a female character. Indeed, she plays a significant role in the theatric drama we call Data Quality. A while back, I was in a hurry to get some updated retirement account balances and had to reset my password in order to get into my account. I provided my email account and requested the password reset, expecting an email to shortly follow with instructions and a hyperlink to change my password. The problem was that I didn’t get the email, with the link to reset the password, until the next day (long after I needed the information).
From January 23-25, the Los Angeles Homeless Services Authority (LAHSA) conducted their annual count of unsheltered homeless with the help of more than 8000 volunteers. On the evening of the 24th, I joined the #TheyCountWillYou effort in Los Angeles to count the homeless in the city of Cudahy. As you’ll see in the description of steps conducted the process is thorough and intentional.
Need more info? See results of the last survey.
